The invention relates to a method for identifying spatially extensive image features that are not pixelwise contiguous in digital images according to the claim 1.
Background of the invention is the process of inspecting a printed image in which process there is typically created a reference image in advance and this is compared with a digital image of a printed matter. By a comparison of these two images there is produced a deviation image whose pixels have assigned thereto as brightness values the difference between the respective brightness values of the respectively corresponding pixels of the reference image and of the image of the printed matter. In known methods according to the prior art the respective deviation image is a grayscale image whose pixels indicate the respective color or brightness difference between the digital image of the printed matter to be checked and the reference image. Pixels of the deviation image which have no deviations hereinafter are referred to as background pixels. Pixels whose respectively assigned deviation values exceed a specified threshold value hereinafter are referred to as foreground pixels. In the prior art the groups of contiguous foreground pixels are called features, blobs or connected components. These are spatially extensive and contiguous image features.
From the prior art there is known a plurality of algorithms for finding blobs, also called blob encoding algorithms, which ascertain information items about the relation of directly neighboring foreground pixels from specified images, in particular from the above-mentioned deviation images. In the prior art these algorithms are also referred to as blob or connected-components analysis, blob or connected-components labelling or coloring. Typical methods of this kind are described for example in Rosenfeld A. and Pfaltz J. L., “Sequential Operations in Digital Picture Processing”, Journal of the ACM.—1966.—p. 471-494, Di Stefano L. and Bulgarelli A., “A simple and efficient connected components labelling algorithm”, 10th International Conference on Image Analysis and Processing.—1999, He Lifeng [et al.], “Fast connected-component labelling”, Pattern Recognition.—2009.—p. 1977-1987, Chang Fu and Chen Chun-Jen, “A Component-Labeling Algorithm Using Contour Tracing Technique”, Proceedings of the Seventh International Conference on Document Analysis and Recognition (ICDAR'03).—[see I.]: IEEE, 2003).
From the prior art it is known to detect blobs as groups of contiguous foreground pixels. Within this meaning, two pixels are considered to be contiguous, when there is a path between them which exclusively consists of foreground pixels. As possible paths there come into consideration merely paths which extend between a pixel and its neighboring foreground pixels within four-neighborhoods or eight-neighborhoods, typically the eight-neighborhood being employed.
A four-neighborhood designates the environment of a pixel, which comprises the upper, lower, right-side and left-side neighboring pixel of the respective pixel. An eight-neighborhood designates an environment around the pixel, which comprises the upper, lower, right-side, left-side as well as all the pixels which meet with the respective pixel at a corner, i.e. the eight-neighborhood further comprises also those pixels which lie above and below the right-side as well as above and below the left-side neighboring pixel.
Any occurring collections of contiguous foreground pixels can be detected as a common, contiguous image feature with means of the prior art. Then, on the basis of the location of the individual image features in the deviation image there can be determined, whether and at most which concrete error value has occurred in the print. The reason or origin of the error cannot be automatically ascertained, which is due to the form and manifestation of the detected feature or blob, and usually requires the visual follow-up check by an expert.
It turns out, however, that accumulations of small, but mutually non-contiguous blobs that are separated by background pixels are caused by similar effects, for example by a large, smudge-like print effect, which is pronounced to different extents over its area and leads to foreground pixels only in partial regions in the deviation image, i.e. that there exist pixels which show no deviation from the reference image. Therefore, the smudge-like print effect appears as a group of a plurality of small smudges separated from each other, which are not detectable as a unit with conventional blob encoding methods. There is thus merely the possibility of detecting these individual partial smudges as such, because the conventional blob detection methods employ only direct neighborhood-relations within the meaning of a four- or eight-neighborhood. It can thus occur that each of these occurring small smudges of foreground pixels alone is not large enough so as to ultimately effect a classification as a printing error, the printing error cannot be recognized as such.